Bibliographic record
Abstract
This paper develops a North–South trade model with heterogeneous labour and horizontally differentiated products, and compares the implications of two policies: Southern intellectual property rights (IPR) and Northern immigration policy, with the latter aiming to attract Southern talent as a means of pre‐empting imitation. Individuals self‐select into becoming entrepreneurs and innovate (imitate) in the North (South). The likelihood of imitation depends on product quality, imitator's talent and IPR strength. Several interrelated channels of competition are identified. Allowing high‐talent migration when IPR protection in the South is weak shifts imitation to low‐quality products and innovation to high‐quality products. The outcome is in stark contrast to the policy of strengthening Southern IPR, which limits low‐talent imitation in the South and encourages low‐quality innovation in the North. Migration also increases the income of low‐talent entrepreneurs, as well as the average quality of products imitated by high‐talent entrepreneurs in the South. Global income rises with migration, but is not guaranteed to rise with stronger Southern IPR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".